Overview

Dataset statistics

Number of variables39
Number of observations1355800
Missing cells10
Missing cells (%)< 0.1%
Total size in memory178.4 MiB
Average record size in memory138.0 B

Variable types

Categorical26
Numeric12
DateTime1

Alerts

Channel is highly overall correlated with contract_typeHigh correlation
approved_with_condition_flag is highly overall correlated with conditions_metHigh correlation
avginvoice_l6m is highly overall correlated with avgnetpay_l9m and 5 other fieldsHigh correlation
avgnetpay_l9m is highly overall correlated with avginvoice_l6m and 11 other fieldsHigh correlation
bad is highly overall correlated with credit_score and 4 other fieldsHigh correlation
client_type is highly overall correlated with avgnetpay_l9m and 16 other fieldsHigh correlation
conditions_met is highly overall correlated with approved_with_condition_flag and 1 other fieldsHigh correlation
contract_type is highly overall correlated with Channel and 7 other fieldsHigh correlation
credit_score is highly overall correlated with bad and 5 other fieldsHigh correlation
credit_score_band is highly overall correlated with credit_score and 1 other fieldsHigh correlation
customer_value is highly overall correlated with avgnetpay_l9m and 7 other fieldsHigh correlation
financed_deal_flag is highly overall correlated with client_type and 7 other fieldsHigh correlation
income_band is highly overall correlated with client_type and 3 other fieldsHigh correlation
internal_bscore is highly overall correlated with avgnetpay_l9m and 18 other fieldsHigh correlation
internal_risk_ranking is highly overall correlated with client_type and 3 other fieldsHigh correlation
invoice_amount is highly overall correlated with avginvoice_l6m and 8 other fieldsHigh correlation
maxarrs_l12m_flag is highly overall correlated with internal_bscore and 6 other fieldsHigh correlation
mediannetpay_l6m is highly overall correlated with avginvoice_l6m and 11 other fieldsHigh correlation
monthly_income is highly overall correlated with avgnetpay_l9m and 8 other fieldsHigh correlation
months_since_acc_creation_binned is highly overall correlated with client_type and 7 other fieldsHigh correlation
monthssincemrrdpayment_flag is highly overall correlated with internal_bscore and 6 other fieldsHigh correlation
no_active_subs is highly overall correlated with client_typeHigh correlation
obs_totalother is highly overall correlated with avginvoice_l6m and 5 other fieldsHigh correlation
portfolio is highly overall correlated with bad and 2 other fieldsHigh correlation
returned_debit_flag is highly overall correlated with internal_bscore and 6 other fieldsHigh correlation
suminvoice_l3m is highly overall correlated with avginvoice_l6m and 11 other fieldsHigh correlation
suminvoice_q1_to_q4 is highly overall correlated with avgnetpay_l9m and 9 other fieldsHigh correlation
sumnetpay_l3m is highly overall correlated with avgnetpay_l9m and 12 other fieldsHigh correlation
thinfile is highly overall correlated with income_bandHigh correlation
times0_l3m is highly overall correlated with bad and 12 other fieldsHigh correlation
times0_l6m is highly overall correlated with bad and 10 other fieldsHigh correlation
timesrdpay_l6m_flag is highly overall correlated with internal_bscore and 6 other fieldsHigh correlation
total_payment_reversals_flag is highly overall correlated with internal_bscore and 6 other fieldsHigh correlation
contract_type is highly imbalanced (72.0%)Imbalance
conditions_met is highly imbalanced (65.3%)Imbalance
thinfile is highly imbalanced (53.9%)Imbalance
no_lines_applied is highly skewed (γ1 = 377.1342865)Skewed
invoice_amount has 74268 (5.5%) zerosZeros
obs_totalother has 105978 (7.8%) zerosZeros
suminvoice_l3m has 67746 (5.0%) zerosZeros
avginvoice_l6m has 67257 (5.0%) zerosZeros
sumnetpay_l3m has 342212 (25.2%) zerosZeros
avgnetpay_l9m has 339504 (25.0%) zerosZeros
mediannetpay_l6m has 349695 (25.8%) zerosZeros
monthly_income has 55712 (4.1%) zerosZeros

Reproduction

Analysis started2026-01-09 21:27:35.555051
Analysis finished2026-01-09 21:31:09.524155
Duration3 minutes and 33.97 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

client_type
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
NEW
829798 
EXISTING
526002 

Length

Max length8
Median length3
Mean length4.9398215
Min length3

Characters and Unicode

Total characters6697410
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEXISTING
2nd rowEXISTING
3rd rowEXISTING
4th rowEXISTING
5th rowEXISTING

Common Values

ValueCountFrequency (%)
NEW829798
61.2%
EXISTING526002
38.8%

Length

2026-01-09T21:31:09.620868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-09T21:31:09.693677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
new829798
61.2%
existing526002
38.8%

Most occurring characters

ValueCountFrequency (%)
N1355800
20.2%
E1355800
20.2%
I1052004
15.7%
W829798
12.4%
X526002
 
7.9%
S526002
 
7.9%
T526002
 
7.9%
G526002
 
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)6697410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N1355800
20.2%
E1355800
20.2%
I1052004
15.7%
W829798
12.4%
X526002
 
7.9%
S526002
 
7.9%
T526002
 
7.9%
G526002
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6697410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N1355800
20.2%
E1355800
20.2%
I1052004
15.7%
W829798
12.4%
X526002
 
7.9%
S526002
 
7.9%
T526002
 
7.9%
G526002
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6697410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N1355800
20.2%
E1355800
20.2%
I1052004
15.7%
W829798
12.4%
X526002
 
7.9%
S526002
 
7.9%
T526002
 
7.9%
G526002
 
7.9%

contract_type
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
INDIVIDUAL
1289925 
BUSINESS
 
65875

Length

Max length10
Median length10
Mean length9.9028249
Min length8

Characters and Unicode

Total characters13426250
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBUSINESS
2nd rowBUSINESS
3rd rowBUSINESS
4th rowBUSINESS
5th rowBUSINESS

Common Values

ValueCountFrequency (%)
INDIVIDUAL1289925
95.1%
BUSINESS65875
 
4.9%

Length

2026-01-09T21:31:09.785380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-09T21:31:09.867743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
individual1289925
95.1%
business65875
 
4.9%

Most occurring characters

ValueCountFrequency (%)
I3935650
29.3%
D2579850
19.2%
N1355800
 
10.1%
U1355800
 
10.1%
V1289925
 
9.6%
A1289925
 
9.6%
L1289925
 
9.6%
S197625
 
1.5%
B65875
 
0.5%
E65875
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)13426250
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I3935650
29.3%
D2579850
19.2%
N1355800
 
10.1%
U1355800
 
10.1%
V1289925
 
9.6%
A1289925
 
9.6%
L1289925
 
9.6%
S197625
 
1.5%
B65875
 
0.5%
E65875
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13426250
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I3935650
29.3%
D2579850
19.2%
N1355800
 
10.1%
U1355800
 
10.1%
V1289925
 
9.6%
A1289925
 
9.6%
L1289925
 
9.6%
S197625
 
1.5%
B65875
 
0.5%
E65875
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13426250
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I3935650
29.3%
D2579850
19.2%
N1355800
 
10.1%
U1355800
 
10.1%
V1289925
 
9.6%
A1289925
 
9.6%
L1289925
 
9.6%
S197625
 
1.5%
B65875
 
0.5%
E65875
 
0.5%

no_lines_applied
Real number (ℝ)

Skewed 

Distinct139
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1842713
Minimum0
Maximum9000
Zeros263
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size20.7 MiB
2026-01-09T21:31:09.967218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum9000
Range9000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18.440741
Coefficient of variation (CV)15.571382
Kurtosis173063.35
Mean1.1842713
Median Absolute Deviation (MAD)0
Skewness377.13429
Sum1605635
Variance340.06093
MonotonicityNot monotonic
2026-01-09T21:31:10.115019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11303705
96.2%
243490
 
3.2%
32843
 
0.2%
41244
 
0.1%
5960
 
0.1%
10503
 
< 0.1%
6487
 
< 0.1%
20290
 
< 0.1%
0263
 
< 0.1%
7248
 
< 0.1%
Other values (129)1767
 
0.1%
ValueCountFrequency (%)
0263
 
< 0.1%
11303705
96.2%
243490
 
3.2%
32843
 
0.2%
41244
 
0.1%
5960
 
0.1%
6487
 
< 0.1%
7248
 
< 0.1%
8220
 
< 0.1%
9117
 
< 0.1%
ValueCountFrequency (%)
90004
 
< 0.1%
50001
 
< 0.1%
25981
 
< 0.1%
25001
 
< 0.1%
15801
 
< 0.1%
150036
< 0.1%
13421
 
< 0.1%
11001
 
< 0.1%
10001
 
< 0.1%
8002
 
< 0.1%

financed_deal_flag
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
Y
888097 
N
467703 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1355800
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y888097
65.5%
N467703
34.5%

Length

2026-01-09T21:31:10.280305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-09T21:31:10.403760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
y888097
65.5%
n467703
34.5%

Most occurring characters

ValueCountFrequency (%)
Y888097
65.5%
N467703
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y888097
65.5%
N467703
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y888097
65.5%
N467703
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y888097
65.5%
N467703
34.5%

sales_region
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
KWAZULU NATAL
431499 
GAUTENG
348881 
NORTHERN GAUTENG
140878 
WESTERN CAPE
127840 
LIMPOPO
95050 
Other values (3)
211652 

Length

Max length19
Median length16
Mean length10.900908
Min length5

Characters and Unicode

Total characters14779451
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOTHER
2nd rowGAUTENG
3rd rowNORTHERN GAUTENG
4th rowNORTHERN GAUTENG
5th rowNORTHERN GAUTENG

Common Values

ValueCountFrequency (%)
KWAZULU NATAL431499
31.8%
GAUTENG348881
25.7%
NORTHERN GAUTENG140878
 
10.4%
WESTERN CAPE127840
 
9.4%
LIMPOPO95050
 
7.0%
OTHER83183
 
6.1%
MPUMALANGA64723
 
4.8%
EASTERN CAPE REGION63746
 
4.7%

Length

2026-01-09T21:31:10.561464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-09T21:31:10.722971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
gauteng489759
22.4%
kwazulu431499
19.8%
natal431499
19.8%
cape191586
 
8.8%
northern140878
 
6.5%
western127840
 
5.9%
limpopo95050
 
4.4%
other83183
 
3.8%
mpumalanga64723
 
3.0%
eastern63746
 
2.9%

Most occurring characters

ValueCountFrequency (%)
A2233757
15.1%
N1523069
10.3%
U1417480
9.6%
E1352324
9.2%
T1336905
9.0%
G1107987
 
7.5%
L1022771
 
6.9%
827709
 
5.6%
R620271
 
4.2%
W559339
 
3.8%
Other values (9)2777839
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)14779451
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A2233757
15.1%
N1523069
10.3%
U1417480
9.6%
E1352324
9.2%
T1336905
9.0%
G1107987
 
7.5%
L1022771
 
6.9%
827709
 
5.6%
R620271
 
4.2%
W559339
 
3.8%
Other values (9)2777839
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14779451
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A2233757
15.1%
N1523069
10.3%
U1417480
9.6%
E1352324
9.2%
T1336905
9.0%
G1107987
 
7.5%
L1022771
 
6.9%
827709
 
5.6%
R620271
 
4.2%
W559339
 
3.8%
Other values (9)2777839
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14779451
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A2233757
15.1%
N1523069
10.3%
U1417480
9.6%
E1352324
9.2%
T1336905
9.0%
G1107987
 
7.5%
L1022771
 
6.9%
827709
 
5.6%
R620271
 
4.2%
W559339
 
3.8%
Other values (9)2777839
18.8%

approved_with_condition_flag
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
N
1184273 
Y
171527 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1355800
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowY
4th rowY
5th rowN

Common Values

ValueCountFrequency (%)
N1184273
87.3%
Y171527
 
12.7%

Length

2026-01-09T21:31:10.966029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-09T21:31:11.100557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
n1184273
87.3%
y171527
 
12.7%

Most occurring characters

ValueCountFrequency (%)
N1184273
87.3%
Y171527
 
12.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N1184273
87.3%
Y171527
 
12.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N1184273
87.3%
Y171527
 
12.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N1184273
87.3%
Y171527
 
12.7%

conditions_met
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
N
1267669 
Y
 
88131

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1355800
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowY
4th rowY
5th rowN

Common Values

ValueCountFrequency (%)
N1267669
93.5%
Y88131
 
6.5%

Length

2026-01-09T21:31:11.255792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-09T21:31:11.384742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
n1267669
93.5%
y88131
 
6.5%

Most occurring characters

ValueCountFrequency (%)
N1267669
93.5%
Y88131
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N1267669
93.5%
Y88131
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N1267669
93.5%
Y88131
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N1267669
93.5%
Y88131
 
6.5%
Distinct44
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.7 MiB
Minimum2021-06-01 00:00:00
Maximum2025-02-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-01-09T21:31:11.510089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:31:11.734289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)

portfolio
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
P0700
579043 
P0600
179241 
P0420
172189 
P0500
141884 
P0400
98990 
Other values (2)
184453 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters6779000
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP0700
2nd rowP0700
3rd rowP0700
4th rowP0700
5th rowP0700

Common Values

ValueCountFrequency (%)
P0700579043
42.7%
P0600179241
 
13.2%
P0420172189
 
12.7%
P0500141884
 
10.5%
P040098990
 
7.3%
P005097008
 
7.2%
OTHER87445
 
6.4%

Length

2026-01-09T21:31:11.951633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-09T21:31:12.088759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
p0700579043
42.7%
p0600179241
 
13.2%
p0420172189
 
12.7%
p0500141884
 
10.5%
p040098990
 
7.3%
p005097008
 
7.2%
other87445
 
6.4%

Most occurring characters

ValueCountFrequency (%)
03632876
53.6%
P1268355
 
18.7%
7579043
 
8.5%
4271179
 
4.0%
5238892
 
3.5%
6179241
 
2.6%
2172189
 
2.5%
O87445
 
1.3%
T87445
 
1.3%
H87445
 
1.3%
Other values (2)174890
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)6779000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
03632876
53.6%
P1268355
 
18.7%
7579043
 
8.5%
4271179
 
4.0%
5238892
 
3.5%
6179241
 
2.6%
2172189
 
2.5%
O87445
 
1.3%
T87445
 
1.3%
H87445
 
1.3%
Other values (2)174890
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6779000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
03632876
53.6%
P1268355
 
18.7%
7579043
 
8.5%
4271179
 
4.0%
5238892
 
3.5%
6179241
 
2.6%
2172189
 
2.5%
O87445
 
1.3%
T87445
 
1.3%
H87445
 
1.3%
Other values (2)174890
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6779000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
03632876
53.6%
P1268355
 
18.7%
7579043
 
8.5%
4271179
 
4.0%
5238892
 
3.5%
6179241
 
2.6%
2172189
 
2.5%
O87445
 
1.3%
T87445
 
1.3%
H87445
 
1.3%
Other values (2)174890
 
2.6%

Channel
Categorical

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
Shops
560345 
Direct
262316 
Dealers
201232 
Online
168541 
National Chains
93579 

Length

Max length18
Median length15
Mean length6.9739947
Min length5

Characters and Unicode

Total characters9455342
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBusiness Solutions
2nd rowBusiness Solutions
3rd rowBusiness Solutions
4th rowBusiness Solutions
5th rowBusiness Solutions

Common Values

ValueCountFrequency (%)
Shops560345
41.3%
Direct262316
19.3%
Dealers201232
 
14.8%
Online168541
 
12.4%
National Chains93579
 
6.9%
Business Solutions69787
 
5.1%

Length

2026-01-09T21:31:12.274487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-09T21:31:13.095074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
shops560345
36.9%
direct262316
17.3%
dealers201232
 
13.2%
online168541
 
11.1%
national93579
 
6.2%
chains93579
 
6.2%
business69787
 
4.6%
solutions69787
 
4.6%

Most occurring characters

ValueCountFrequency (%)
s1134304
12.0%
e903108
 
9.6%
o793498
 
8.4%
i757589
 
8.0%
n663814
 
7.0%
h653924
 
6.9%
S630132
 
6.7%
p560345
 
5.9%
l533139
 
5.6%
a481969
 
5.1%
Other values (10)2343520
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)9455342
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s1134304
12.0%
e903108
 
9.6%
o793498
 
8.4%
i757589
 
8.0%
n663814
 
7.0%
h653924
 
6.9%
S630132
 
6.7%
p560345
 
5.9%
l533139
 
5.6%
a481969
 
5.1%
Other values (10)2343520
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9455342
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s1134304
12.0%
e903108
 
9.6%
o793498
 
8.4%
i757589
 
8.0%
n663814
 
7.0%
h653924
 
6.9%
S630132
 
6.7%
p560345
 
5.9%
l533139
 
5.6%
a481969
 
5.1%
Other values (10)2343520
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9455342
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s1134304
12.0%
e903108
 
9.6%
o793498
 
8.4%
i757589
 
8.0%
n663814
 
7.0%
h653924
 
6.9%
S630132
 
6.7%
p560345
 
5.9%
l533139
 
5.6%
a481969
 
5.1%
Other values (10)2343520
24.8%

Term
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
36
696217 
24
615242 
Ot
 
44341

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2711600
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row24
2nd row24
3rd row24
4th row24
5th row24

Common Values

ValueCountFrequency (%)
36696217
51.4%
24615242
45.4%
Ot44341
 
3.3%

Length

2026-01-09T21:31:13.275224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-09T21:31:13.409527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
36696217
51.4%
24615242
45.4%
ot44341
 
3.3%

Most occurring characters

ValueCountFrequency (%)
3696217
25.7%
6696217
25.7%
2615242
22.7%
4615242
22.7%
O44341
 
1.6%
t44341
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)2711600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3696217
25.7%
6696217
25.7%
2615242
22.7%
4615242
22.7%
O44341
 
1.6%
t44341
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2711600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3696217
25.7%
6696217
25.7%
2615242
22.7%
4615242
22.7%
O44341
 
1.6%
t44341
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2711600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3696217
25.7%
6696217
25.7%
2615242
22.7%
4615242
22.7%
O44341
 
1.6%
t44341
 
1.6%

Phones
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
1
829230 
0
405518 
2+
121052 

Length

Max length2
Median length1
Mean length1.0892846
Min length1

Characters and Unicode

Total characters1476852
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1829230
61.2%
0405518
29.9%
2+121052
 
8.9%

Length

2026-01-09T21:31:13.548004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-09T21:31:13.683891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1829230
61.2%
0405518
29.9%
2121052
 
8.9%

Most occurring characters

ValueCountFrequency (%)
1829230
56.1%
0405518
27.5%
2121052
 
8.2%
+121052
 
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)1476852
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1829230
56.1%
0405518
27.5%
2121052
 
8.2%
+121052
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1476852
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1829230
56.1%
0405518
27.5%
2121052
 
8.2%
+121052
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1476852
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1829230
56.1%
0405518
27.5%
2121052
 
8.2%
+121052
 
8.2%

Bank
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
Bank_A
548787 
Bank_B
274208 
Bank_C
196882 
Bank_D
167545 
Bank_E
116617 

Length

Max length6
Median length6
Mean length5.9618225
Min length5

Characters and Unicode

Total characters8083039
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBank_E
2nd rowBank_E
3rd rowBank_B
4th rowBank_B
5th rowBank_B

Common Values

ValueCountFrequency (%)
Bank_A548787
40.5%
Bank_B274208
20.2%
Bank_C196882
 
14.5%
Bank_D167545
 
12.4%
Bank_E116617
 
8.6%
Other51761
 
3.8%

Length

2026-01-09T21:31:13.846819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-09T21:31:13.994341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
bank_a548787
40.5%
bank_b274208
20.2%
bank_c196882
 
14.5%
bank_d167545
 
12.4%
bank_e116617
 
8.6%
other51761
 
3.8%

Most occurring characters

ValueCountFrequency (%)
B1578247
19.5%
a1304039
16.1%
n1304039
16.1%
k1304039
16.1%
_1304039
16.1%
A548787
 
6.8%
C196882
 
2.4%
D167545
 
2.1%
E116617
 
1.4%
O51761
 
0.6%
Other values (4)207044
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)8083039
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B1578247
19.5%
a1304039
16.1%
n1304039
16.1%
k1304039
16.1%
_1304039
16.1%
A548787
 
6.8%
C196882
 
2.4%
D167545
 
2.1%
E116617
 
1.4%
O51761
 
0.6%
Other values (4)207044
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8083039
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B1578247
19.5%
a1304039
16.1%
n1304039
16.1%
k1304039
16.1%
_1304039
16.1%
A548787
 
6.8%
C196882
 
2.4%
D167545
 
2.1%
E116617
 
1.4%
O51761
 
0.6%
Other values (4)207044
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8083039
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B1578247
19.5%
a1304039
16.1%
n1304039
16.1%
k1304039
16.1%
_1304039
16.1%
A548787
 
6.8%
C196882
 
2.4%
D167545
 
2.1%
E116617
 
1.4%
O51761
 
0.6%
Other values (4)207044
 
2.6%

credit_score
Real number (ℝ)

High correlation 

Distinct195
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean644.40435
Minimum529
Maximum723
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.7 MiB
2026-01-09T21:31:14.169365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum529
5-th percentile587
Q1617
median644
Q3673
95-th percentile702
Maximum723
Range194
Interquartile range (IQR)56

Descriptive statistics

Standard deviation35.936447
Coefficient of variation (CV)0.055766923
Kurtosis-0.84080966
Mean644.40435
Median Absolute Deviation (MAD)28
Skewness-0.045752169
Sum8.7368341 × 108
Variance1291.4283
MonotonicityNot monotonic
2026-01-09T21:31:14.305201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61614813
 
1.1%
63314763
 
1.1%
58914264
 
1.1%
64013843
 
1.0%
62313717
 
1.0%
62013651
 
1.0%
60213551
 
1.0%
65413424
 
1.0%
62613323
 
1.0%
63013275
 
1.0%
Other values (185)1217176
89.8%
ValueCountFrequency (%)
5291
 
< 0.1%
5302
 
< 0.1%
5313
 
< 0.1%
5323
 
< 0.1%
5337
 
< 0.1%
5346
 
< 0.1%
5354
 
< 0.1%
53619
< 0.1%
5378
< 0.1%
53816
< 0.1%
ValueCountFrequency (%)
723628
 
< 0.1%
722181
 
< 0.1%
72119
 
< 0.1%
7201258
0.1%
719687
 
0.1%
718780
 
0.1%
7171533
0.1%
7161732
0.1%
7152350
0.2%
7142065
0.2%

bad
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.7 MiB
0
1023146 
1
332654 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1355800
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01023146
75.5%
1332654
 
24.5%

Length

2026-01-09T21:31:14.437722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-09T21:31:14.511505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01023146
75.5%
1332654
 
24.5%

Most occurring characters

ValueCountFrequency (%)
01023146
75.5%
1332654
 
24.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01023146
75.5%
1332654
 
24.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01023146
75.5%
1332654
 
24.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01023146
75.5%
1332654
 
24.5%

times0_l3m
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
1
559363 
3
472452 
0
198739 
2
125246 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1355800
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
1559363
41.3%
3472452
34.8%
0198739
 
14.7%
2125246
 
9.2%

Length

2026-01-09T21:31:14.648479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-09T21:31:14.738802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1559363
41.3%
3472452
34.8%
0198739
 
14.7%
2125246
 
9.2%

Most occurring characters

ValueCountFrequency (%)
1559363
41.3%
3472452
34.8%
0198739
 
14.7%
2125246
 
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1559363
41.3%
3472452
34.8%
0198739
 
14.7%
2125246
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1559363
41.3%
3472452
34.8%
0198739
 
14.7%
2125246
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1559363
41.3%
3472452
34.8%
0198739
 
14.7%
2125246
 
9.2%

suminvoice_q1_to_q4
Real number (ℝ)

High correlation 

Distinct32074
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.499178
Minimum-1
Maximum331.15
Zeros760
Zeros (%)0.1%
Negative869272
Negative (%)64.1%
Memory size20.7 MiB
2026-01-09T21:31:14.847289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3107.98
95-th percentile217.0305
Maximum331.15
Range332.15
Interquartile range (IQR)108.98

Descriptive statistics

Standard deviation81.793613
Coefficient of variation (CV)1.5882508
Kurtosis1.8587771
Mean51.499178
Median Absolute Deviation (MAD)0
Skewness1.5502925
Sum69822586
Variance6690.1951
MonotonicityNot monotonic
2026-01-09T21:31:14.982403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1869156
64.1%
331.1527119
 
2.0%
1001985
 
0.1%
0760
 
0.1%
133.33265
 
< 0.1%
66.67169
 
< 0.1%
33.3393
 
< 0.1%
106.587
 
< 0.1%
107.0286
 
< 0.1%
112.2683
 
< 0.1%
Other values (32064)455997
33.6%
ValueCountFrequency (%)
-1869156
64.1%
-0.982
 
< 0.1%
-0.971
 
< 0.1%
-0.961
 
< 0.1%
-0.921
 
< 0.1%
-0.91
 
< 0.1%
-0.881
 
< 0.1%
-0.871
 
< 0.1%
-0.851
 
< 0.1%
-0.832
 
< 0.1%
ValueCountFrequency (%)
331.1527119
2.0%
331.133
 
< 0.1%
331.121
 
< 0.1%
331.12
 
< 0.1%
331.091
 
< 0.1%
331.082
 
< 0.1%
331.074
 
< 0.1%
331.052
 
< 0.1%
331.043
 
< 0.1%
331.031
 
< 0.1%

returned_debit_flag
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.7 MiB
0
966210 
1
389590 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1355800
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0966210
71.3%
1389590
28.7%

Length

2026-01-09T21:31:15.109320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-09T21:31:15.182481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0966210
71.3%
1389590
28.7%

Most occurring characters

ValueCountFrequency (%)
0966210
71.3%
1389590
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0966210
71.3%
1389590
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0966210
71.3%
1389590
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0966210
71.3%
1389590
28.7%

internal_risk_ranking
Categorical

High correlation 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
0
835967 
1
248673 
2
 
58069
4
 
53279
3
 
48532
Other values (6)
111280 

Length

Max length2
Median length1
Mean length1.0003548
Min length1

Characters and Unicode

Total characters1356281
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0835967
61.7%
1248673
 
18.3%
258069
 
4.3%
453279
 
3.9%
348532
 
3.6%
526277
 
1.9%
926005
 
1.9%
622458
 
1.7%
718633
 
1.4%
817426
 
1.3%

Length

2026-01-09T21:31:15.262760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0835967
61.7%
1248673
 
18.3%
258069
 
4.3%
453279
 
3.9%
348532
 
3.6%
526277
 
1.9%
926005
 
1.9%
622458
 
1.7%
718633
 
1.4%
817426
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0835967
61.6%
1248673
 
18.3%
258069
 
4.3%
453279
 
3.9%
348532
 
3.6%
926967
 
2.0%
526277
 
1.9%
622458
 
1.7%
718633
 
1.4%
817426
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1356281
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0835967
61.6%
1248673
 
18.3%
258069
 
4.3%
453279
 
3.9%
348532
 
3.6%
926967
 
2.0%
526277
 
1.9%
622458
 
1.7%
718633
 
1.4%
817426
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1356281
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0835967
61.6%
1248673
 
18.3%
258069
 
4.3%
453279
 
3.9%
348532
 
3.6%
926967
 
2.0%
526277
 
1.9%
622458
 
1.7%
718633
 
1.4%
817426
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1356281
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0835967
61.6%
1248673
 
18.3%
258069
 
4.3%
453279
 
3.9%
348532
 
3.6%
926967
 
2.0%
526277
 
1.9%
622458
 
1.7%
718633
 
1.4%
817426
 
1.3%

no_active_subs
Categorical

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
1
661595 
2
351385 
3
132948 
5+
130529 
4
75314 

Length

Max length2
Median length1
Mean length1.0962745
Min length1

Characters and Unicode

Total characters1486329
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5+
2nd row5+
3rd row5+
4th row5+
5th row5+

Common Values

ValueCountFrequency (%)
1661595
48.8%
2351385
25.9%
3132948
 
9.8%
5+130529
 
9.6%
475314
 
5.6%
04029
 
0.3%

Length

2026-01-09T21:31:15.364432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-09T21:31:15.448687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1661595
48.8%
2351385
25.9%
3132948
 
9.8%
5130529
 
9.6%
475314
 
5.6%
04029
 
0.3%

Most occurring characters

ValueCountFrequency (%)
1661595
44.5%
2351385
23.6%
3132948
 
8.9%
5130529
 
8.8%
+130529
 
8.8%
475314
 
5.1%
04029
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1486329
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1661595
44.5%
2351385
23.6%
3132948
 
8.9%
5130529
 
8.8%
+130529
 
8.8%
475314
 
5.1%
04029
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1486329
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1661595
44.5%
2351385
23.6%
3132948
 
8.9%
5130529
 
8.8%
+130529
 
8.8%
475314
 
5.1%
04029
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1486329
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1661595
44.5%
2351385
23.6%
3132948
 
8.9%
5130529
 
8.8%
+130529
 
8.8%
475314
 
5.1%
04029
 
0.3%

invoice_amount
Real number (ℝ)

High correlation  Zeros 

Distinct262355
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1063.3128
Minimum-4964.28
Maximum-0
Zeros74268
Zeros (%)5.5%
Negative1281532
Negative (%)94.5%
Memory size20.7 MiB
2026-01-09T21:31:15.574225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-4964.28
5-th percentile-4168.485
Q1-1268
median-649.01
Q3-349
95-th percentile0
Maximum-0
Range4964.28
Interquartile range (IQR)919

Descriptive statistics

Standard deviation1159.5183
Coefficient of variation (CV)-1.0904771
Kurtosis3.8382469
Mean-1063.3128
Median Absolute Deviation (MAD)359.06
Skewness-2.0584079
Sum-1.4416395 × 109
Variance1344482.6
MonotonicityNot monotonic
2026-01-09T21:31:15.746863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-074268
 
5.5%
-4964.2854233
 
4.0%
-29911192
 
0.8%
-1997349
 
0.5%
-199.016932
 
0.5%
-3996572
 
0.5%
-308.996226
 
0.5%
-2295793
 
0.4%
-299.015287
 
0.4%
-2494650
 
0.3%
Other values (262345)1173298
86.5%
ValueCountFrequency (%)
-4964.2854233
4.0%
-4964.221
 
< 0.1%
-4963.831
 
< 0.1%
-4963.741
 
< 0.1%
-4963.71
 
< 0.1%
-4963.681
 
< 0.1%
-4963.541
 
< 0.1%
-4963.471
 
< 0.1%
-4963.461
 
< 0.1%
-4963.421
 
< 0.1%
ValueCountFrequency (%)
-074268
5.5%
-0.0152
 
< 0.1%
-0.028
 
< 0.1%
-0.032
 
< 0.1%
-0.043
 
< 0.1%
-0.052
 
< 0.1%
-0.061
 
< 0.1%
-0.071
 
< 0.1%
-0.084
 
< 0.1%
-0.093
 
< 0.1%

obs_totalother
Real number (ℝ)

High correlation  Zeros 

Distinct242244
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean846.55519
Minimum-10789.45
Maximum3795.42
Zeros105978
Zeros (%)7.8%
Negative73
Negative (%)< 0.1%
Memory size20.7 MiB
2026-01-09T21:31:15.882388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-10789.45
5-th percentile0
Q1345.9
median599.86
Q31080.57
95-th percentile2618.2805
Maximum3795.42
Range14584.87
Interquartile range (IQR)734.67

Descriptive statistics

Standard deviation799.97285
Coefficient of variation (CV)0.94497424
Kurtosis3.6095732
Mean846.55519
Median Absolute Deviation (MAD)315.18
Skewness1.836272
Sum1.1477595 × 109
Variance639956.57
MonotonicityNot monotonic
2026-01-09T21:31:16.013447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0105978
 
7.8%
3795.4227117
 
2.0%
8.693730
 
0.3%
85.221942
 
0.1%
17.381526
 
0.1%
199.131297
 
0.1%
476.521102
 
0.1%
373.041064
 
0.1%
351.311031
 
0.1%
320.87978
 
0.1%
Other values (242234)1210035
89.2%
ValueCountFrequency (%)
-10789.451
< 0.1%
-10542.831
< 0.1%
-3409.531
< 0.1%
-2621.081
< 0.1%
-2126.541
< 0.1%
-2076.761
< 0.1%
-1371.551
< 0.1%
-1054.021
< 0.1%
-1036.191
< 0.1%
-938.711
< 0.1%
ValueCountFrequency (%)
3795.4227117
2.0%
3795.231
 
< 0.1%
3795.151
 
< 0.1%
3795.011
 
< 0.1%
3794.991
 
< 0.1%
3794.861
 
< 0.1%
3794.762
 
< 0.1%
3794.751
 
< 0.1%
3794.741
 
< 0.1%
3794.71
 
< 0.1%

times0_l6m
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
1
554428 
6
437228 
0
197216 
2
77362 
5
60635 
Other values (2)
 
28931

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1355800
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6
2nd row6
3rd row6
4th row6
5th row6

Common Values

ValueCountFrequency (%)
1554428
40.9%
6437228
32.2%
0197216
 
14.5%
277362
 
5.7%
560635
 
4.5%
417807
 
1.3%
311124
 
0.8%

Length

2026-01-09T21:31:16.142792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-09T21:31:16.233790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1554428
40.9%
6437228
32.2%
0197216
 
14.5%
277362
 
5.7%
560635
 
4.5%
417807
 
1.3%
311124
 
0.8%

Most occurring characters

ValueCountFrequency (%)
1554428
40.9%
6437228
32.2%
0197216
 
14.5%
277362
 
5.7%
560635
 
4.5%
417807
 
1.3%
311124
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1554428
40.9%
6437228
32.2%
0197216
 
14.5%
277362
 
5.7%
560635
 
4.5%
417807
 
1.3%
311124
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1554428
40.9%
6437228
32.2%
0197216
 
14.5%
277362
 
5.7%
560635
 
4.5%
417807
 
1.3%
311124
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1554428
40.9%
6437228
32.2%
0197216
 
14.5%
277362
 
5.7%
560635
 
4.5%
417807
 
1.3%
311124
 
0.8%

suminvoice_l3m
Real number (ℝ)

High correlation  Zeros 

Distinct407621
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2201.4747
Minimum0
Maximum16614.44
Zeros67746
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size20.7 MiB
2026-01-09T21:31:16.355853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q1368.99
median772.39
Q32244.26
95-th percentile10399.493
Maximum16614.44
Range16614.44
Interquartile range (IQR)1875.27

Descriptive statistics

Standard deviation3539.2766
Coefficient of variation (CV)1.6076844
Kurtosis7.6545429
Mean2201.4747
Median Absolute Deviation (MAD)513.41
Skewness2.7876087
Sum2.9847595 × 109
Variance12526479
MonotonicityNot monotonic
2026-01-09T21:31:16.487305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
067746
 
5.0%
16614.4440675
 
3.0%
29910710
 
0.8%
1996986
 
0.5%
199.016756
 
0.5%
3996377
 
0.5%
308.995909
 
0.4%
2295609
 
0.4%
299.015133
 
0.4%
2494522
 
0.3%
Other values (407611)1195377
88.2%
ValueCountFrequency (%)
067746
5.0%
0.0149
 
< 0.1%
0.022
 
< 0.1%
0.043
 
< 0.1%
0.052
 
< 0.1%
0.061
 
< 0.1%
0.071
 
< 0.1%
0.085
 
< 0.1%
0.093
 
< 0.1%
0.12
 
< 0.1%
ValueCountFrequency (%)
16614.4440675
3.0%
16614.181
 
< 0.1%
16614.041
 
< 0.1%
16613.891
 
< 0.1%
16613.651
 
< 0.1%
16613.551
 
< 0.1%
16612.81
 
< 0.1%
16612.231
 
< 0.1%
16611.761
 
< 0.1%
16611.041
 
< 0.1%

avginvoice_l6m
Real number (ℝ)

High correlation  Zeros 

Distinct234040
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean853.42187
Minimum0
Maximum4103.56
Zeros67257
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size20.7 MiB
2026-01-09T21:31:16.625661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.2985
Q1308.67
median527.955
Q3973.33
95-th percentile3401.572
Maximum4103.56
Range4103.56
Interquartile range (IQR)664.66

Descriptive statistics

Standard deviation937.46296
Coefficient of variation (CV)1.0984754
Kurtosis4.5253984
Mean853.42187
Median Absolute Deviation (MAD)271.045
Skewness2.2066638
Sum1.1570694 × 109
Variance878836.81
MonotonicityNot monotonic
2026-01-09T21:31:16.773271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
067257
 
5.0%
4103.5654233
 
4.0%
2999757
 
0.7%
1996435
 
0.5%
199.016327
 
0.5%
3995453
 
0.4%
308.995215
 
0.4%
2295052
 
0.4%
299.014658
 
0.3%
2494231
 
0.3%
Other values (234030)1187182
87.6%
ValueCountFrequency (%)
067257
5.0%
0.0140
 
< 0.1%
0.022
 
< 0.1%
0.031
 
< 0.1%
0.042
 
< 0.1%
0.053
 
< 0.1%
0.062
 
< 0.1%
0.071
 
< 0.1%
0.084
 
< 0.1%
0.092
 
< 0.1%
ValueCountFrequency (%)
4103.5654233
4.0%
4103.31
 
< 0.1%
4103.262
 
< 0.1%
4103.191
 
< 0.1%
4103.091
 
< 0.1%
4103.081
 
< 0.1%
4103.061
 
< 0.1%
4102.911
 
< 0.1%
4102.871
 
< 0.1%
4102.791
 
< 0.1%

sumnetpay_l3m
Real number (ℝ)

High correlation  Zeros 

Distinct344144
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1619.3438
Minimum-0
Maximum9904.89
Zeros342212
Zeros (%)25.2%
Negative0
Negative (%)0.0%
Memory size20.7 MiB
2026-01-09T21:31:16.910052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0
5-th percentile-0
Q1-0
median349.42
Q31889.91
95-th percentile9904.89
Maximum9904.89
Range9904.89
Interquartile range (IQR)1889.91

Descriptive statistics

Standard deviation2647.8128
Coefficient of variation (CV)1.6351147
Kurtosis3.282452
Mean1619.3438
Median Absolute Deviation (MAD)349.42
Skewness2.0568605
Sum2.1955063 × 109
Variance7010912.7
MonotonicityNot monotonic
2026-01-09T21:31:17.043625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0342212
 
25.2%
9904.8967792
 
5.0%
181544
 
0.1%
120.751327
 
0.1%
200966
 
0.1%
597852
 
0.1%
328785
 
0.1%
273706
 
0.1%
185683
 
0.1%
100.88654
 
< 0.1%
Other values (344134)938279
69.2%
ValueCountFrequency (%)
-0342212
25.2%
1.42109 × 10-141
 
< 0.1%
2.84217 × 10-141
 
< 0.1%
3.55271 × 10-143
 
< 0.1%
4.9738 × 10-141
 
< 0.1%
5.68434 × 10-145
 
< 0.1%
7.10543 × 10-141
 
< 0.1%
8.52651 × 10-141
 
< 0.1%
1.13687 × 10-137
 
< 0.1%
1.42109 × 10-131
 
< 0.1%
ValueCountFrequency (%)
9904.8967792
5.0%
9904.621
 
< 0.1%
9904.531
 
< 0.1%
9904.281
 
< 0.1%
9904.21
 
< 0.1%
99041
 
< 0.1%
9903.721
 
< 0.1%
9903.691
 
< 0.1%
9903.191
 
< 0.1%
9903.181
 
< 0.1%

avgnetpay_l9m
Real number (ℝ)

High correlation  Zeros 

Distinct204941
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean584.66066
Minimum-0
Maximum3440.01
Zeros339504
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size20.7 MiB
2026-01-09T21:31:17.172807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0
5-th percentile-0
Q1-0
median267.05
Q3636.3425
95-th percentile3139.741
Maximum3440.01
Range3440.01
Interquartile range (IQR)636.3425

Descriptive statistics

Standard deviation863.95341
Coefficient of variation (CV)1.4777006
Kurtosis4.0148791
Mean584.66066
Median Absolute Deviation (MAD)267.05
Skewness2.1743319
Sum7.9268292 × 108
Variance746415.5
MonotonicityNot monotonic
2026-01-09T21:31:17.303182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0339504
 
25.0%
3440.0161012
 
4.5%
181472
 
0.1%
120.751303
 
0.1%
2001015
 
0.1%
328733
 
0.1%
185666
 
< 0.1%
273636
 
< 0.1%
300583
 
< 0.1%
230561
 
< 0.1%
Other values (204931)948315
69.9%
ValueCountFrequency (%)
-0339504
25.0%
0.0127
 
< 0.1%
0.021
 
< 0.1%
0.031
 
< 0.1%
0.091
 
< 0.1%
0.11
 
< 0.1%
0.113
 
< 0.1%
0.131
 
< 0.1%
0.191
 
< 0.1%
0.21
 
< 0.1%
ValueCountFrequency (%)
3440.0161012
4.5%
3439.981
 
< 0.1%
3439.921
 
< 0.1%
3439.91
 
< 0.1%
3439.841
 
< 0.1%
3439.81
 
< 0.1%
3439.761
 
< 0.1%
3439.651
 
< 0.1%
3439.641
 
< 0.1%
3439.621
 
< 0.1%

mediannetpay_l6m
Real number (ℝ)

High correlation  Zeros 

Distinct225191
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean560.68558
Minimum-993.825
Maximum3117.11
Zeros349695
Zeros (%)25.8%
Negative342
Negative (%)< 0.1%
Memory size20.7 MiB
2026-01-09T21:31:17.435513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-993.825
5-th percentile-0
Q1-0
median257.42
Q3618.12125
95-th percentile3117.11
Maximum3117.11
Range4110.935
Interquartile range (IQR)618.12125

Descriptive statistics

Standard deviation813.1919
Coefficient of variation (CV)1.4503528
Kurtosis3.3747385
Mean560.68558
Median Absolute Deviation (MAD)257.42
Skewness2.0525995
Sum7.6017751 × 108
Variance661281.06
MonotonicityNot monotonic
2026-01-09T21:31:17.561153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0349695
 
25.8%
3117.1167791
 
5.0%
2992851
 
0.2%
1991966
 
0.1%
308.991669
 
0.1%
181539
 
0.1%
2001313
 
0.1%
120.751302
 
0.1%
208.991109
 
0.1%
209902
 
0.1%
Other values (225181)925663
68.3%
ValueCountFrequency (%)
-993.8251
< 0.1%
-689.191
< 0.1%
-484.061
< 0.1%
-459.3951
< 0.1%
-4371
< 0.1%
-4311
< 0.1%
-420.491
< 0.1%
-397.1851
< 0.1%
-393.911
< 0.1%
-388.031
< 0.1%
ValueCountFrequency (%)
3117.1167791
5.0%
3117.0651
 
< 0.1%
3117.0151
 
< 0.1%
3117.011
 
< 0.1%
3116.9751
 
< 0.1%
3116.9251
 
< 0.1%
3116.8251
 
< 0.1%
3116.8151
 
< 0.1%
3116.81
 
< 0.1%
3116.761
 
< 0.1%

internal_bscore
Real number (ℝ)

High correlation 

Distinct215
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean621.29175
Minimum502
Maximum727
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.7 MiB
2026-01-09T21:31:17.694595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum502
5-th percentile539
Q1592
median602
Q3670
95-th percentile721
Maximum727
Range225
Interquartile range (IQR)78

Descriptive statistics

Standard deviation55.97059
Coefficient of variation (CV)0.090087451
Kurtosis-0.93053632
Mean621.29175
Median Absolute Deviation (MAD)36
Skewness0.32096629
Sum8.4234736 × 108
Variance3132.707
MonotonicityNot monotonic
2026-01-09T21:31:17.838976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
602432366
31.9%
539183971
 
13.6%
57591124
 
6.7%
72243350
 
3.2%
61342626
 
3.1%
72121513
 
1.6%
58619623
 
1.4%
54817529
 
1.3%
71312482
 
0.9%
70211444
 
0.8%
Other values (205)479772
35.4%
ValueCountFrequency (%)
5028
 
< 0.1%
5102
 
< 0.1%
511170
< 0.1%
5124
 
< 0.1%
5131
 
< 0.1%
5151
 
< 0.1%
5176
 
< 0.1%
5181
 
< 0.1%
51926
 
< 0.1%
52031
 
< 0.1%
ValueCountFrequency (%)
7274174
 
0.3%
7265073
 
0.4%
72582
 
< 0.1%
7234
 
< 0.1%
72243350
3.2%
72121513
1.6%
7204513
 
0.3%
719229
 
< 0.1%
718925
 
0.1%
7178367
 
0.6%

monthly_income
Real number (ℝ)

High correlation  Zeros 

Distinct1436
Distinct (%)0.1%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean19820.067
Minimum0
Maximum150000
Zeros55712
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size20.7 MiB
2026-01-09T21:31:17.968062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2000
Q15300
median12800
Q327800
95-th percentile61300
Maximum150000
Range150000
Interquartile range (IQR)22500

Descriptive statistics

Standard deviation19955.36
Coefficient of variation (CV)1.006826
Kurtosis3.7217483
Mean19820.067
Median Absolute Deviation (MAD)8700
Skewness1.7760843
Sum2.6871948 × 1010
Variance3.9821638 × 108
MonotonicityNot monotonic
2026-01-09T21:31:18.100078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
055712
 
4.1%
410014799
 
1.1%
450012516
 
0.9%
420012333
 
0.9%
430011764
 
0.9%
500011705
 
0.9%
540011666
 
0.9%
470011529
 
0.9%
400011511
 
0.8%
440011215
 
0.8%
Other values (1426)1191045
87.8%
ValueCountFrequency (%)
055712
4.1%
10010
 
< 0.1%
20016
 
< 0.1%
30018
 
< 0.1%
40044
 
< 0.1%
50047
 
< 0.1%
600131
 
< 0.1%
70057
 
< 0.1%
80077
 
< 0.1%
900561
 
< 0.1%
ValueCountFrequency (%)
15000029
< 0.1%
1498009
 
< 0.1%
1496001
 
< 0.1%
1495001
 
< 0.1%
1491003
 
< 0.1%
1489001
 
< 0.1%
1488001
 
< 0.1%
1485001
 
< 0.1%
1479001
 
< 0.1%
1477002
 
< 0.1%

income_band
Categorical

High correlation 

Distinct17
Distinct (%)< 0.1%
Missing5
Missing (%)< 0.1%
Memory size11.6 MiB
R4,000 - R5,999
201246 
R10,000 - R14,999
139941 
<R4,000
136395 
R15,000 - R19,999
125514 
R6,000 - R7,999
121102 
Other values (12)
631597 

Length

Max length24
Median length17
Mean length15.651908
Min length7

Characters and Unicode

Total characters21220778
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowR20,000 - R24,999
2nd rowR30,000 - R39,999
3rd rowR40,000 - R49,999
4th rowR30,000 - R39,999
5th rowR40,000 - R49,999

Common Values

ValueCountFrequency (%)
R4,000 - R5,999201246
14.8%
R10,000 - R14,999139941
10.3%
<R4,000136395
10.1%
R15,000 - R19,999125514
9.3%
R6,000 - R7,999121102
8.9%
R30,000 - R39,999115629
8.5%
R20,000 - R24,999107475
7.9%
R25,000 - R29,99984933
6.3%
R8,000 - R9,99978263
 
5.8%
R40,000 - R49,99971177
 
5.2%
Other values (7)174120
12.8%

Length

2026-01-09T21:31:18.229367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1156181
30.1%
r4,000337641
 
8.8%
r5,999201246
 
5.2%
r10,000139941
 
3.6%
r14,999139941
 
3.6%
r15,000125514
 
3.3%
r19,999125514
 
3.3%
r6,000121102
 
3.2%
r7,999121102
 
3.2%
r30,000115629
 
3.0%
Other values (24)1251482
32.6%

Most occurring characters

ValueCountFrequency (%)
04460386
21.0%
94068476
19.2%
2479498
11.7%
R2456264
11.6%
,2456264
11.6%
-1156181
 
5.4%
4727411
 
3.4%
1538417
 
2.5%
5503407
 
2.4%
2384816
 
1.8%
Other values (24)1989658
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)21220778
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
04460386
21.0%
94068476
19.2%
2479498
11.7%
R2456264
11.6%
,2456264
11.6%
-1156181
 
5.4%
4727411
 
3.4%
1538417
 
2.5%
5503407
 
2.4%
2384816
 
1.8%
Other values (24)1989658
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)21220778
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
04460386
21.0%
94068476
19.2%
2479498
11.7%
R2456264
11.6%
,2456264
11.6%
-1156181
 
5.4%
4727411
 
3.4%
1538417
 
2.5%
5503407
 
2.4%
2384816
 
1.8%
Other values (24)1989658
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)21220778
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
04460386
21.0%
94068476
19.2%
2479498
11.7%
R2456264
11.6%
,2456264
11.6%
-1156181
 
5.4%
4727411
 
3.4%
1538417
 
2.5%
5503407
 
2.4%
2384816
 
1.8%
Other values (24)1989658
9.4%

customer_value
Categorical

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
Bronze
929257 
Silver
169109 
Onyx
97950 
Platinum
 
80336
Gold
 
78667

Length

Max length8
Median length6
Mean length5.8569074
Min length3

Characters and Unicode

Total characters7940795
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOnyx
2nd rowOnyx
3rd rowOnyx
4th rowOnyx
5th rowOnyx

Common Values

ValueCountFrequency (%)
Bronze929257
68.5%
Silver169109
 
12.5%
Onyx97950
 
7.2%
Platinum80336
 
5.9%
Gold78667
 
5.8%
Tin481
 
< 0.1%

Length

2026-01-09T21:31:18.333442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-09T21:31:18.414835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
bronze929257
68.5%
silver169109
 
12.5%
onyx97950
 
7.2%
platinum80336
 
5.9%
gold78667
 
5.8%
tin481
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n1108024
14.0%
r1098366
13.8%
e1098366
13.8%
o1007924
12.7%
B929257
11.7%
z929257
11.7%
l328112
 
4.1%
i249926
 
3.1%
S169109
 
2.1%
v169109
 
2.1%
Other values (11)853345
10.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)7940795
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n1108024
14.0%
r1098366
13.8%
e1098366
13.8%
o1007924
12.7%
B929257
11.7%
z929257
11.7%
l328112
 
4.1%
i249926
 
3.1%
S169109
 
2.1%
v169109
 
2.1%
Other values (11)853345
10.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7940795
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n1108024
14.0%
r1098366
13.8%
e1098366
13.8%
o1007924
12.7%
B929257
11.7%
z929257
11.7%
l328112
 
4.1%
i249926
 
3.1%
S169109
 
2.1%
v169109
 
2.1%
Other values (11)853345
10.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7940795
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n1108024
14.0%
r1098366
13.8%
e1098366
13.8%
o1007924
12.7%
B929257
11.7%
z929257
11.7%
l328112
 
4.1%
i249926
 
3.1%
S169109
 
2.1%
v169109
 
2.1%
Other values (11)853345
10.7%

thinfile
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.7 MiB
0
1223424 
1
132376 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1355800
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01223424
90.2%
1132376
 
9.8%

Length

2026-01-09T21:31:18.525573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-09T21:31:18.607439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01223424
90.2%
1132376
 
9.8%

Most occurring characters

ValueCountFrequency (%)
01223424
90.2%
1132376
 
9.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01223424
90.2%
1132376
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01223424
90.2%
1132376
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01223424
90.2%
1132376
 
9.8%

maxarrs_l12m_flag
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.7 MiB
0
1021954 
1
333846 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1355800
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01021954
75.4%
1333846
 
24.6%

Length

2026-01-09T21:31:18.699531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-09T21:31:18.779400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01021954
75.4%
1333846
 
24.6%

Most occurring characters

ValueCountFrequency (%)
01021954
75.4%
1333846
 
24.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01021954
75.4%
1333846
 
24.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01021954
75.4%
1333846
 
24.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01021954
75.4%
1333846
 
24.6%

total_payment_reversals_flag
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.7 MiB
0
966210 
1
389590 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1355800
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0966210
71.3%
1389590
28.7%

Length

2026-01-09T21:31:18.884008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-09T21:31:18.958396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0966210
71.3%
1389590
28.7%

Most occurring characters

ValueCountFrequency (%)
0966210
71.3%
1389590
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0966210
71.3%
1389590
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0966210
71.3%
1389590
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0966210
71.3%
1389590
28.7%

monthssincemrrdpayment_flag
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.7 MiB
0
853580 
1
502220 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1355800
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0853580
63.0%
1502220
37.0%

Length

2026-01-09T21:31:19.048744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-09T21:31:19.121178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0853580
63.0%
1502220
37.0%

Most occurring characters

ValueCountFrequency (%)
0853580
63.0%
1502220
37.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0853580
63.0%
1502220
37.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0853580
63.0%
1502220
37.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0853580
63.0%
1502220
37.0%

timesrdpay_l6m_flag
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.7 MiB
0
936130 
1
419670 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1355800
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0936130
69.0%
1419670
31.0%

Length

2026-01-09T21:31:19.209877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-09T21:31:19.283569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0936130
69.0%
1419670
31.0%

Most occurring characters

ValueCountFrequency (%)
0936130
69.0%
1419670
31.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0936130
69.0%
1419670
31.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0936130
69.0%
1419670
31.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1355800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0936130
69.0%
1419670
31.0%

months_since_acc_creation_binned
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
<3
817827 
100+
213288 
3-40
167897 
41-100
156788 

Length

Max length6
Median length2
Mean length3.0248724
Min length2

Characters and Unicode

Total characters4101122
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100+
2nd row100+
3rd row100+
4th row100+
5th row100+

Common Values

ValueCountFrequency (%)
<3817827
60.3%
100+213288
 
15.7%
3-40167897
 
12.4%
41-100156788
 
11.6%

Length

2026-01-09T21:31:19.365004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-09T21:31:19.444639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3817827
60.3%
100213288
 
15.7%
3-40167897
 
12.4%
41-100156788
 
11.6%

Most occurring characters

ValueCountFrequency (%)
3985724
24.0%
0908049
22.1%
<817827
19.9%
1526864
12.8%
-324685
 
7.9%
4324685
 
7.9%
+213288
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)4101122
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3985724
24.0%
0908049
22.1%
<817827
19.9%
1526864
12.8%
-324685
 
7.9%
4324685
 
7.9%
+213288
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4101122
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3985724
24.0%
0908049
22.1%
<817827
19.9%
1526864
12.8%
-324685
 
7.9%
4324685
 
7.9%
+213288
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4101122
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3985724
24.0%
0908049
22.1%
<817827
19.9%
1526864
12.8%
-324685
 
7.9%
4324685
 
7.9%
+213288
 
5.2%

credit_score_band
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
660-688
306538 
610-635
302412 
635-660
298989 
<610
258272 
688+
189589 

Length

Max length7
Median length7
Mean length6.0090109
Min length4

Characters and Unicode

Total characters8147017
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row688+
2nd row688+
3rd row688+
4th row688+
5th row688+

Common Values

ValueCountFrequency (%)
660-688306538
22.6%
610-635302412
22.3%
635-660298989
22.1%
<610258272
19.0%
688+189589
14.0%

Length

2026-01-09T21:31:19.540628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-09T21:31:19.624273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
660-688306538
22.6%
610-635302412
22.3%
635-660298989
22.1%
610258272
19.0%
688189589
14.0%

Most occurring characters

ValueCountFrequency (%)
62869266
35.2%
01166211
14.3%
8992254
 
12.2%
-907939
 
11.1%
3601401
 
7.4%
5601401
 
7.4%
1560684
 
6.9%
<258272
 
3.2%
+189589
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)8147017
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
62869266
35.2%
01166211
14.3%
8992254
 
12.2%
-907939
 
11.1%
3601401
 
7.4%
5601401
 
7.4%
1560684
 
6.9%
<258272
 
3.2%
+189589
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8147017
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
62869266
35.2%
01166211
14.3%
8992254
 
12.2%
-907939
 
11.1%
3601401
 
7.4%
5601401
 
7.4%
1560684
 
6.9%
<258272
 
3.2%
+189589
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8147017
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
62869266
35.2%
01166211
14.3%
8992254
 
12.2%
-907939
 
11.1%
3601401
 
7.4%
5601401
 
7.4%
1560684
 
6.9%
<258272
 
3.2%
+189589
 
2.3%

Interactions

2026-01-09T21:30:54.451214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:10.880827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:14.399791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:18.914659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:22.452632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:26.338438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:30.524786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:34.441606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:37.919861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:41.653894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:46.749551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:50.427677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:54.746160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:11.102088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:14.755070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:19.204331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:23.078696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:26.635827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:30.893794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:35.073711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:38.226424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:41.918649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:47.104321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:50.756127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:55.037664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:11.319111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:15.146186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:19.476643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:23.372746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:26.950394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:31.300888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:35.329388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:38.547591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-09T21:30:51.097090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:55.320562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:11.583320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:15.564573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:19.761045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:23.644449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:27.260127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:31.703809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:35.589763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-09T21:30:42.755202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-09T21:30:55.666545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:11.839162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:15.991764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:20.049198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:23.957936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:27.535868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:32.097235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:35.832972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:39.175029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-09T21:30:52.784242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:58.310727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:12.881989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:17.592864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-09T21:30:33.275208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:36.792233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:40.459325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:45.028756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:48.988834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:53.118065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:58.774778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:13.161170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:18.020060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:21.498988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:25.470269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:29.373749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:33.571241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:37.039790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:40.814806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:45.489109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:49.246808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:53.459015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:59.239663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:13.429789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:18.316051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:21.809399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:25.748472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:29.759975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:33.847281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:37.314597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:41.129264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:45.954265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:49.846824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:53.770633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:59.661250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:14.016833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:18.628515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:22.129771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:26.050890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:30.152679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:34.146152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:37.620502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:41.389831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:46.402694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:50.112041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T21:30:54.101131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-09T21:31:20.458636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
BankChannelPhonesTermapproved_with_condition_flagavginvoice_l6mavgnetpay_l9mbadclient_typeconditions_metcontract_typecredit_scorecredit_score_bandcustomer_valuefinanced_deal_flagincome_bandinternal_bscoreinternal_risk_rankinginvoice_amountmaxarrs_l12m_flagmediannetpay_l6mmonthly_incomemonths_since_acc_creation_binnedmonthssincemrrdpayment_flagno_active_subsno_lines_appliedobs_totalotherportfolioreturned_debit_flagsales_regionsuminvoice_l3msuminvoice_q1_to_q4sumnetpay_l3mthinfiletimes0_l3mtimes0_l6mtimesrdpay_l6m_flagtotal_payment_reversals_flag
Bank1.0000.1040.0560.0800.0570.1340.1510.2900.3250.0870.2220.2140.2360.1720.3820.2080.1870.1710.1420.1870.1500.1910.2050.2490.1320.0120.1080.1940.3050.0720.1530.1470.1570.1550.2050.1610.2910.305
Channel0.1041.0000.2650.2080.2570.2230.2110.3100.2130.3660.6680.1670.1800.1670.3580.1630.1430.1160.2250.1770.2140.1530.1160.1790.1610.0120.1130.2590.2130.3590.2210.0980.1940.1350.1630.1340.1980.213
Phones0.0560.2651.0000.3580.1380.1250.1230.0110.0700.0550.1110.1240.1120.0790.1390.0680.0830.0830.1320.0120.1280.0640.0670.0510.2540.0060.1270.3310.0490.0970.0740.0700.0770.0420.0540.0580.0490.049
Term0.0800.2080.3581.0000.0970.1230.0960.0960.0730.0370.1570.1160.1090.0890.0830.0850.0950.0840.1200.0700.0930.0800.0610.0930.1070.0040.0920.2450.0970.1240.0950.0670.0880.0140.0660.0680.0960.097
approved_with_condition_flag0.0570.2570.1380.0971.0000.1910.1890.0970.0840.6790.3540.1800.1750.1830.2550.1700.1230.1370.1980.0430.1750.1380.0960.0280.1610.0140.1370.3510.0640.0850.2270.0970.2020.0700.1050.1120.0590.064
avginvoice_l6m0.1340.2230.1250.1230.1911.0000.5140.1740.4700.3220.6120.3340.1970.4650.3680.1950.3120.167-0.9090.2110.5190.4080.2950.1490.3440.1130.5770.1970.1850.0630.8720.4000.4800.1230.2770.2110.1680.185
avgnetpay_l9m0.1510.2110.1230.0960.1890.5141.0000.3290.7180.3020.5880.4860.2380.5730.4310.2160.7050.247-0.5640.1410.9910.5340.4350.1020.4130.1180.5410.2500.2230.0680.6690.6540.9700.1910.3960.2920.1810.223
bad0.2900.3100.0110.0960.0970.1740.3291.0000.3590.0090.1160.5100.4990.3500.4830.4190.5390.3730.2160.3560.3630.3470.3520.3090.2870.0030.2380.5270.4040.1950.2770.3510.3360.2330.5030.5040.3800.404
client_type0.3250.2130.0700.0730.0840.4700.7180.3591.0000.0490.2240.4980.4910.8250.5060.5430.9200.9310.6170.0530.7060.5000.9450.0500.7960.0070.4980.7270.2740.1530.7590.9140.8720.2290.9070.9520.2120.274
conditions_met0.0870.3660.0550.0370.6790.3220.3020.0090.0491.0000.5480.1070.1020.2470.0500.1770.0900.1420.3210.0140.2850.1750.0420.0540.2090.0180.0990.1240.0450.0570.3400.0680.2920.0130.0340.0450.0430.045
contract_type0.2220.6680.1110.1570.3540.6120.5880.1160.2240.5481.0000.2730.2650.4910.1230.3790.2760.3230.6060.0530.5610.3760.2190.1170.4370.0260.1010.2050.1310.1390.6460.2370.5680.0230.2030.2110.1230.131
credit_score0.2140.1670.1240.1160.1800.3340.4860.5100.4980.1070.2731.0000.8570.2730.6830.2590.6200.232-0.4000.2820.4780.6850.3390.3320.2040.0970.3300.3500.4080.0830.4690.4710.5160.3120.3240.2350.3910.408
credit_score_band0.2360.1800.1120.1090.1750.1970.2380.4990.4910.1020.2650.8571.0000.3000.6740.3770.3420.3410.2180.2760.2400.3250.3330.3270.2240.0040.1800.4150.4020.1070.2350.2560.2500.2860.3190.2830.3860.402
customer_value0.1720.1670.0790.0890.1830.4650.5730.3500.8250.2470.4910.2730.3001.0000.4520.2980.5250.6490.4330.1770.5740.2850.5280.1750.4010.0040.2880.3110.3490.0770.5280.4030.6090.1960.4910.3920.3120.349
financed_deal_flag0.3820.3580.1390.0830.2550.3680.4310.4830.5060.0500.1230.6830.6740.4521.0000.5800.5570.4930.4490.2180.4460.4940.4890.2220.4480.0040.4340.9610.3470.2120.4290.4790.4660.2840.5330.5440.3130.347
income_band0.2080.1630.0680.0850.1700.1950.2160.4190.5430.1770.3790.2590.3770.2980.5801.0000.2110.1890.2070.1950.2130.8020.3340.2390.2400.0070.2030.2810.3560.0730.2200.1810.2220.6560.3200.2330.3240.356
internal_bscore0.1870.1430.0830.0950.1230.3120.7050.5390.9200.0900.2760.6200.3420.5250.5570.2111.0000.484-0.4440.8130.6910.5980.6980.7850.3590.0930.3730.3210.8870.0760.5950.7570.7780.2490.7930.5810.8570.887
internal_risk_ranking0.1710.1160.0830.0840.1370.1670.2470.3730.9310.1420.3230.2320.3410.6490.4930.1890.4841.0000.2060.3450.2420.1850.6560.3110.3570.0110.2190.3050.3860.0630.2530.3280.2920.2180.5580.4360.3700.386
invoice_amount0.1420.2250.1320.1200.198-0.909-0.5640.2160.6170.3210.606-0.4000.2180.4330.4490.207-0.4440.2061.0000.209-0.558-0.4690.3620.1550.374-0.115-0.6540.2360.1910.066-0.946-0.569-0.5800.1640.3470.2610.1710.191
maxarrs_l12m_flag0.1870.1770.0120.0700.0430.2110.1410.3560.0530.0140.0530.2820.2760.1770.2180.1950.8130.3450.2091.0000.2230.1580.0630.6090.0390.0090.0600.2390.5710.1100.0290.0990.0670.1050.7630.8580.6100.571
mediannetpay_l6m0.1500.2140.1280.0930.1750.5190.9910.3630.7060.2850.5610.4780.2400.5740.4460.2130.6910.242-0.5580.2231.0000.5250.4260.0950.4060.1160.5350.2530.2280.0730.6600.6350.9600.2050.4060.2970.1840.228
monthly_income0.1910.1530.0640.0800.1380.4080.5340.3470.5000.1750.3760.6850.3250.2850.4940.8020.5980.185-0.4690.1580.5251.0000.3100.2100.2270.1110.3500.2450.3110.0630.5330.5020.5610.2700.2880.2100.2840.311
months_since_acc_creation_binned0.2050.1160.0670.0610.0960.2950.4350.3520.9450.0420.2190.3390.3330.5280.4890.3340.6980.6560.3620.0630.4260.3101.0000.1300.4780.0080.2930.4220.2810.0880.4480.5460.5190.2180.5290.5650.2310.281
monthssincemrrdpayment_flag0.2490.1790.0510.0930.0280.1490.1020.3090.0500.0540.1170.3320.3270.1750.2220.2390.7850.3110.1550.6090.0950.2100.1301.0000.0660.0070.0340.2430.8280.1150.0840.0840.0920.0840.5190.5490.8730.828
no_active_subs0.1320.1610.2540.1070.1610.3440.4130.2870.7960.2090.4370.2040.2240.4010.4480.2400.3590.3570.3740.0390.4060.2270.4780.0661.0000.0090.2670.2930.2230.0760.4160.3490.4380.1870.4240.3470.1770.223
no_lines_applied0.0120.0120.0060.0040.0140.1130.1180.0030.0070.0180.0260.0970.0040.0040.0040.0070.0930.011-0.1150.0090.1160.1110.0080.0070.0091.000-0.0260.0030.0020.0050.1140.0680.1110.0010.0030.0070.0050.002
obs_totalother0.1080.1130.1270.0920.1370.5770.5410.2380.4980.0990.1010.3300.1800.2880.4340.2030.3730.219-0.6540.0600.5350.3500.2930.0340.267-0.0261.0000.2250.1520.0540.6400.4320.5420.1750.2710.2180.1200.152
portfolio0.1940.2590.3310.2450.3510.1970.2500.5270.7270.1240.2050.3500.4150.3110.9610.2810.3210.3050.2360.2390.2530.2450.4220.2430.2930.0030.2251.0000.3880.1290.2520.3000.2760.3000.4270.3150.3480.388
returned_debit_flag0.3050.2130.0490.0970.0640.1850.2230.4040.2740.0450.1310.4080.4020.3490.3470.3560.8870.3860.1910.5710.2280.3110.2810.8280.2230.0020.1520.3881.0000.1360.2220.2750.2620.1450.6320.6310.9481.000
sales_region0.0720.3590.0970.1240.0850.0630.0680.1950.1530.0570.1390.0830.1070.0770.2120.0730.0760.0630.0660.1100.0730.0630.0880.1150.0760.0050.0540.1290.1361.0000.0620.0570.0630.0760.1050.0780.1280.136
suminvoice_l3m0.1530.2210.0740.0950.2270.8720.6690.2770.7590.3400.6460.4690.2350.5280.4290.2200.5950.253-0.9460.0290.6600.5330.4480.0840.4160.1140.6400.2520.2220.0621.0000.7090.7120.1860.3990.3000.1750.222
suminvoice_q1_to_q40.1470.0980.0700.0670.0970.4000.6540.3510.9140.0680.2370.4710.2560.4030.4790.1810.7570.328-0.5690.0990.6350.5020.5460.0840.3490.0680.4320.3000.2750.0570.7091.0000.7490.2150.5000.3780.2270.275
sumnetpay_l3m0.1570.1940.0770.0880.2020.4800.9700.3360.8720.2920.5680.5160.2500.6090.4660.2220.7780.292-0.5800.0670.9600.5610.5190.0920.4380.1110.5420.2760.2620.0630.7120.7491.0000.2050.4780.3510.2050.262
thinfile0.1550.1350.0420.0140.0700.1230.1910.2330.2290.0130.0230.3120.2860.1960.2840.6560.2490.2180.1640.1050.2050.2700.2180.0840.1870.0010.1750.3000.1450.0760.1860.2150.2051.0000.2460.2500.1280.145
times0_l3m0.2050.1630.0540.0660.1050.2770.3960.5030.9070.0340.2030.3240.3190.4910.5330.3200.7930.5580.3470.7630.4060.2880.5290.5190.4240.0030.2710.4270.6320.1050.3990.5000.4780.2461.0000.9590.6000.632
times0_l6m0.1610.1340.0580.0680.1120.2110.2920.5040.9520.0450.2110.2350.2830.3920.5440.2330.5810.4360.2610.8580.2970.2100.5650.5490.3470.0070.2180.3150.6310.0780.3000.3780.3510.2500.9591.0000.6310.631
timesrdpay_l6m_flag0.2910.1980.0490.0960.0590.1680.1810.3800.2120.0430.1230.3910.3860.3120.3130.3240.8570.3700.1710.6100.1840.2840.2310.8730.1770.0050.1200.3480.9480.1280.1750.2270.2050.1280.6000.6311.0000.948
total_payment_reversals_flag0.3050.2130.0490.0970.0640.1850.2230.4040.2740.0450.1310.4080.4020.3490.3470.3560.8870.3860.1910.5710.2280.3110.2810.8280.2230.0020.1520.3881.0000.1360.2220.2750.2620.1450.6320.6310.9481.000

Missing values

2026-01-09T21:31:00.174166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-09T21:31:04.226266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-01-09T21:31:09.474763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.